{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ROKGACL5KBEQTRTRJYLF7JH23M","short_pith_number":"pith:ROKGACL5","schema_version":"1.0","canonical_sha256":"8b9460097d504909c6714e165fa4fadb1042ac13a5bcb72280477d2352c5f6bd","source":{"kind":"arxiv","id":"2501.18726","version":1},"attestation_state":"computed","paper":{"title":"Strong and Controllable 3D Motion Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Canxuan Gang","submitted_at":"2025-01-30T20:06:30Z","abstract_excerpt":"Human motion generation is a significant pursuit in generative computer vision with widespread applications in film-making, video games, AR/VR, and human-robot interaction. Current methods mainly utilize either diffusion-based generative models or autoregressive models for text-to-motion generation. However, they face two significant challenges: (1) The generation process is time-consuming, posing a major obstacle for real-time applications such as gaming, robot manipulation, and other online settings. (2) These methods typically learn a relative motion representation guided by text, making it"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.18726","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-30T20:06:30Z","cross_cats_sorted":[],"title_canon_sha256":"dc5dad8f242658a1147e60939ccce2d77ad9ccb260e5dcb6fdbfe4fa8b952c1f","abstract_canon_sha256":"d6683852f9fdf5893509e858b237dbf1ed9a355ac81431fe1d59d4d2ed6f1a6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:50.467985Z","signature_b64":"IPgmOhpbOtuJ3Vp88eJMQ9gftOTsENltNRhCJooTwqD6448FiKYywA/oZLBafeeQwLOK9lVKAhDHnx9GU+XpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b9460097d504909c6714e165fa4fadb1042ac13a5bcb72280477d2352c5f6bd","last_reissued_at":"2026-07-05T10:07:50.467513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:50.467513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Strong and Controllable 3D Motion Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Canxuan Gang","submitted_at":"2025-01-30T20:06:30Z","abstract_excerpt":"Human motion generation is a significant pursuit in generative computer vision with widespread applications in film-making, video games, AR/VR, and human-robot interaction. Current methods mainly utilize either diffusion-based generative models or autoregressive models for text-to-motion generation. However, they face two significant challenges: (1) The generation process is time-consuming, posing a major obstacle for real-time applications such as gaming, robot manipulation, and other online settings. (2) These methods typically learn a relative motion representation guided by text, making it"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.18726","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.18726/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.18726","created_at":"2026-07-05T10:07:50.467571+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.18726v1","created_at":"2026-07-05T10:07:50.467571+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.18726","created_at":"2026-07-05T10:07:50.467571+00:00"},{"alias_kind":"pith_short_12","alias_value":"ROKGACL5KBEQ","created_at":"2026-07-05T10:07:50.467571+00:00"},{"alias_kind":"pith_short_16","alias_value":"ROKGACL5KBEQTRTR","created_at":"2026-07-05T10:07:50.467571+00:00"},{"alias_kind":"pith_short_8","alias_value":"ROKGACL5","created_at":"2026-07-05T10:07:50.467571+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M","json":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M.json","graph_json":"https://pith.science/api/pith-number/ROKGACL5KBEQTRTRJYLF7JH23M/graph.json","events_json":"https://pith.science/api/pith-number/ROKGACL5KBEQTRTRJYLF7JH23M/events.json","paper":"https://pith.science/paper/ROKGACL5"},"agent_actions":{"view_html":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M","download_json":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M.json","view_paper":"https://pith.science/paper/ROKGACL5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.18726&json=true","fetch_graph":"https://pith.science/api/pith-number/ROKGACL5KBEQTRTRJYLF7JH23M/graph.json","fetch_events":"https://pith.science/api/pith-number/ROKGACL5KBEQTRTRJYLF7JH23M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M/action/storage_attestation","attest_author":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M/action/author_attestation","sign_citation":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M/action/citation_signature","submit_replication":"https://pith.science/pith/ROKGACL5KBEQTRTRJYLF7JH23M/action/replication_record"}},"created_at":"2026-07-05T10:07:50.467571+00:00","updated_at":"2026-07-05T10:07:50.467571+00:00"}